## Measuring clutch performance

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• Is it possible now to quantify the level of a team or player s clutch ability with the methods available to this group?
Message 1 of 19 , Nov 29, 2001
Is it possible now to quantify the level of a team or player's clutch
ability with the methods available to this group?
• First you d have to figure out what clutch is... is clutch hitting a big shot that changes momentum in the 2nd quarter? is clutch hitting a game winning shot,
Message 2 of 19 , Nov 29, 2001
First you'd have to figure out what clutch is... is clutch hitting a big shot that changes momentum in the 2nd quarter? is clutch hitting a game winning shot, grabbing a big rebound to solidify a win? I don't really think you can quantify clutch, without physically watching a game 2 or 3 times. Determining momentum shifts, (ending a run with a huge three, that's clutch) determining when a game is really close, and when it's not (games that end up with 6-7 point differentials often time were within 2 with under 5 mins to go, or were huge differentials, so you can't just tell by the end score whether it was a close game), and figuring out when a team just makes a break down that allows you to make a play that seems big (that college game, where a guy's knees buckled due to dehydration or something, and the dribbler drove right by him for an easy shot or layup or something... is that clutch, or just a defensive breakdown?). I think you could probably come up with a calculation of clutchness, both team wise and player wise, but I don't think it'd be very accurate, because a lot of those things are things that happen spontaneously, and thus you really can't set it at a percentage (times a player came up with a momentum change, divided by times he didn't, just wouldn't work), and a sum total could be a little accurate, but what if your team is really good and you just blow teams out on a regular basis by building up leads early in the game and holding onto them. I don't really see an easy way to measure this, without watching game tapes and drawing opinions, not statistics.

~Ray

In a message dated 11/30/01 12:18:00 AM Eastern Standard Time, dlirag@... writes:

Is it possible now to quantify the level of a team or player's clutch
ability with the methods available to this group?

• ... shot ... First of all, hi group. I m new here. Secondly, I ve been trying to come up with a definition of a clutch shooter that _is_ quantifiable. Here
Message 3 of 19 , Nov 29, 2001
: > Is it possible now to quantify the level of a team or player's clutch
: > ability with the methods available to this group?
:
: : First you'd have to figure out what clutch is... is clutch hitting a big
shot
: that changes momentum in the 2nd quarter? is clutch hitting a game winning
: shot, grabbing a big rebound to solidify a win? I don't really think you can
: quantify clutch, without physically watching a game 2 or 3 times.

First of all, hi group. I'm new here.

Secondly, I've been trying to come up with a definition of a "clutch shooter"
that _is_ quantifiable. Here are a few ideas:

1. a. A clutch shooter is one who shooting % rises in the 4thQ
b. A clutch shooter is one who shooting % rises in the last 5 minutes.

2. A clutch shooter is one who's shooting % rises when his team is within 10
points of the other.

3. A clutch shooter is one who shooting % rises with less than 5 seconds left on
the shot clock.

I know this doesn't cover all the meanings in the word "clutch", but I think
these are still useful. Would you expect a good clutch shooter to have his
shooting % to _drop_ in the last quarter?

In any case, #1 is pretty easy to calculate using the game logs. #2 and #3 might
be impossible without actually watching the games.
• Well, being the pain in the butt I am, here is my argument to what you said. The last 5 minutes of a game can be clutch, or it can be garbage time. Garbage
Message 4 of 19 , Nov 29, 2001
Well, being the pain in the butt I am, here is my argument to what you said. The last 5 minutes of a game can be clutch, or it can be garbage time. Garbage time = no defense, = higher FG%. I think if you did things the way you wrote them up, you'd see guys who you wouldn't expect up there because their team is involved in a lot of blow outs, and guys see their only time in games at the end of games, where limited defense is being played more so than a lot of good players probably stand out at that time with tougher defense. Since most games are generally played within 10 points of each other, that really doesn't seem to eliminate much. Granted there is a lot of time when the differential is larger also, but I think 10 points is a bit too broad almost. I think to get the best reference, it would have to be differential in relationship to time... within 10 points during the 2nd and 3rd quarters, and within 6 points during the 1st and 4th quarters. The 2nd and 3rd quarters are usually where teams make their big runs, and momentum seems to be very shifty over long runs (4th quarters see a lot of 4-6 point streaks, 2nd and 3rd quarters i think see more 9-12 point streaks) and thus the differentials tend to sway largely during those quarters, where as the 1st and 4th tend to be a bit more methodical, as each team tries to set a pace, and as a result a smaller margin of error is created, especially in the fourth quarter. #3 is hard to disagree with. Also, I may expect a clutch shooters' shooting percentage not to rise during the 4th quarter. 4th quarters are typically defensive (I would love to see an average break down of scoring per quarter, I bet the 4th quarter is probably 4-5 points lower than any other quarter) orientated quarters, as a result maintaining a FG% may be equivalent to increasing a FG%. Also, a FG% may drop, if a coach feels that a player is clutch, and thus they want them to take more shots, tougher shots, and bad shots because they want the ball in that players hands. Very few would argue against Iverson being a clutch player, and yet I've seen him shoot poorly by trying to do too much because nobody else in the lineup is capable of doing anything.

~Ray

In a message dated 11/30/01 1:05:11 AM Eastern Standard Time,
edkupfer@... writes:

First of all, hi group. I'm new here.

Secondly, I've been trying to come up with a definition of a "clutch shooter"
that _is_ quantifiable. Here are a few ideas:

1. a.  A clutch shooter is one who shooting % rises in the 4thQ
b.  A clutch shooter is one who shooting % rises in the last 5 minutes.

2. A clutch shooter is one who's shooting % rises when his team is within 10
points of the other.

3. A clutch shooter is one who shooting % rises with less than 5 seconds left on
the shot clock.

I know this doesn't cover all the meanings in the word "clutch", but I think
these are still useful. Would you expect a good clutch shooter to have his
shooting % to _drop_ in the last quarter?

In any case, #1 is pretty easy to calculate using the game logs. #2 and #3 might
be impossible without actually watching the games.

• Clutch play by a team is often reflected in the percent of close games they win. It might also include the quality of the opponent, and the situation:
Message 5 of 19 , Nov 30, 2001
"Clutch" play by a team is often reflected in the percent of close
games they win. It might also include the quality of the opponent,
and the situation: vying for playoff position, or actually in the
playoffs.

--- In APBR_analysis@y..., dlirag@h... wrote:
> Is it possible now to quantify the level of a team or player's
clutch
> ability with the methods available to this group?

Individual clutch play certainly makes or breaks a reputation,
particularly if one's performance leads to playoff wins and more
opportunity to prove oneself. I have seen some very clutch
performances in losing efforts, however.

The idea of creating a momentum change in the 2nd or 3rd quarter is
curious to me. Only in retrospect can a momentum-change be seen.
The guy who heaves up a 30-foot shot to the dismay of his coach, may
later be called Mr. Clutch because the shot went in, and the game
shifted at that point.

My gut feeling is that pro players in big games do not just stampede
to defeat because of a short run of points by the other team; and if
this does happen, it isn't really to the credit of someone making a
few consecutive shots.

I rather like the simple idea that all contributions are equal: a
backup forward who gets 5 good minutes in the 2nd quarter, by
outplaying his counterpart, may not get a chance in the 4th quarter.
Nevertheless, if he recognizes and performs his job, he was clutch.

When someone says "yo, Reggie choked", and I say "He made 10 of 15
shots", I might then hear "yes, but he missed when it counted".
This to me is nonsense; if Reggie hadn't made 10 of 14, then shot #15
wouldn't have mattered.
And for the record, the shots that go in are the ones that count.

When players do better in the postseason than in the regular season,
they could be said to be clutch. Bill Russell, Michael Jordan,
Hakeem Olajuwon always did better in the postseason, in spite of
increased competition and pressure. Wilt, Oscar, and the Mailman
always did worse.

By this standard, "clutch" is certainly measurable. But there will
always be a place for opinion.
• Just a note... I always like to do a very simple comparison; playoff numbers vs. regular season numbers. If a player can put up the same numbers, or better
Message 6 of 19 , Nov 30, 2001
Just a note... I always like to do a very simple comparison; playoff numbers
vs. regular season numbers. If a player can put up the same numbers, or
better he's clutch. Generally I allow about 5% difference for FG% because
'FG%s go wayyyy down.

~Ray
• ... Actually, at least in baseball and the WNBA, this has not proven to be the case. Generally speaking, bad teams win more close games than any other kind and
Message 7 of 19 , Nov 30, 2001
> "Clutch" play by a team is often reflected in the percent of close
> games they win.

Actually, at least in baseball and the WNBA, this has not proven to be the
case. Generally speaking, bad teams win more close games than any other kind
and good teams win fewer close games than any other kind.

The reason is that bad teams don't win too many games that aren't close. Bad
teams, pretty much by definition, aren't going to blow out better teams.
They're going to play over their heads, while the better team plays below
expectations resulting in a close win for the bad team.

Good teams, on the other hand, routinely blow out their opponents, and
engage in many mor eblot-outs throughout the course of a season than close
games.

This is not to say that bad teams have better winning percentages in close
games than good teams, although there are a number of instances where this
is the case. Last season in the WNBA, the Detroit Shock was 10-22 and 4-4 in
games decided by three points or less. Sacramento was 20-12 and 3-3 in games
decided by three points or less. Drawing the conclusion that both Detroit
and Sacramento are both equally good teams "in the clutch" is inapproriate,
in my opinion. Or, if it does mean that both teams are equally good "in the
clutch," then perhaps having a good clutch team isn't all that important.

Also in the WNBA last season, the Portland Fire were 10-22 on the year, but
4-1 in overtime games. I would imagine that overtime is a decent barometer
of the "pucker factor" in the regular season, but if Portland is such a
clutch team, why are they only 10-22? On the other hand, Charlotte finished
18-14 on the season and advanced to the WNBA Finals but was 0-3 in overtime
games during the regular season.

I'm obviously cherry-picking here, but to say that "clutch" play by a team
is often reflected in the percent of close games it wins isn't supported by
the facts.

Here's a link to a baseball study on the issue
http://www.baseballstuff.com/btf/scholars/ruane/articles/onerun.htm

With regards to players, I looked at Yolanda Griffith and Lisa Leslie last
year and how they performed "in the clutch" to back up my opinion that
Griffith, and not Leslie, should have been the league MVP. I defined "in the
clutch" as being any time in the last 5 minutes of a game where the teams
were separated by no more than 5 points. Admittedly it was pretty arbitrary,
but as you all have discussed, defining "clutch" performance is one of the
larger stumbling block to determining if the ability to perform "in the
clutch" exists.

The first item of note from my study was that out of a possible 185 minutes
for Leslie and 190 for Griffith each played just shy of 80 minutes worth of
"clutch" time. That's two full games in the WNBA. Is that enough of a sample
size, 80 minutes, to be able to determine a player's ability "in the
clutch?" I don't know.

Anyway, Leslie's field goal percentage dropped 140 points "in the clutch"
while Griffith dropped 174. Leslie's rebound average dropped by two, her
assist and blocked shots averages were down slightly, while she increased
her steals average by half a point and decreased her turnover rate from 3.1
to 1.7. Her scoring average decreased by two. She doubled her trips to the
free throw line, but her percentage fell 100 points.

Griffith's rebound average dropped half a board, her assists remained
constant, she blocked no shots during this time (blocking 37 during the rest
of the season) while she picked up an extra half of a steal and decreased
her turnover rate from 2.34 to 1.28. Her scoring average decreased by more
than five points. Her trips to the free throw line decreased slightly, but
she hit essentially the same percentage.

So what does that all mean? I haven't a clue. My gut tells me that the
sample size is just too small to mean anything with regards to most of these
numbers. And while the drop in field goal percentage is alarming, it may
simply have to do with a difference in the way the opposition is defending
these two players. Then again, it might be because these two, as go-to
players, expect to take the shots at the end of the game and tend to force
them as a result.

I forget if it was Bill James, Rob Neyer or another Sabermetrician who did a
study into "clutch" hitting in baseball players using whatever the
definition in is for close and late situations -- something like after the
7th inning down two runs or less. What they found during the years they
studied (1980s) was players like Dane Iorg throughout the top-ten in batting
average in these situations. They also found that there was no consistency
with regards to these batting averages from year to year, leading them to
conclude that "clutch" hitting was not an actual ability. While "clutch"
performances exist, the idea that players have the ability to consistently
perform above expectations "in the clutch" has yet to be proven.

Just found the Rob Neyer article about which I was thinking.
http://www.diamond-mind.com/articles/neyerclutch.htm

John Maxwell
Director of Public Relations
Charlotte Sting
• ... Not only that, baseball teams with a good winning pct. in one-run games generally decline the following season (as myself and several other White Sox fans
Message 8 of 19 , Dec 1, 2001
--- John Maxwell <John.Maxwell@...>
wrote:
> > "Clutch" play by a team is often reflected in the
> percent of close
> > games they win.
>
> Actually, at least in baseball and the WNBA, this
> has not proven to be the
> case. Generally speaking, bad teams win more close
> games than any other kind
> and good teams win fewer close games than any other
> kind.
>
Not only that, baseball teams with a good winning pct.
in one-run games generally decline the following
season (as myself and several other White Sox fans
found out this past summer). I suspect the same is
true in the NBA, though I have never looked at the
subject, nor am I aware of anyone who has.

I would have no idea how to analyze which players are
clutch and which ones aren't. Basketball isn't like
baseball where you can just look at what each player
does in each AB and go from there. In basketball
there's defense, rebounding and passing going on in
addition to shooting. Those things would have to be
looked at also, once clutch situations were defined.

I've always felt "clutch" was one of those terms
people used to describe players they wanted to like.
Jerry West was called Mr. Clutch, despite being on the
losing team in eight NBA finals and winning only once.
This isn't to say West wasn't a clutch player. I just
wonder why West got tagged with Mr. Clutch, when it
was Bill Russell who was the biggest winner of that
time. Probably a racial thing.

Ed

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• ... I agree 100% with the statements that it is a mistake to look at teams records in close games, and to try to call the ones with good records clutch . A
Message 9 of 19 , Dec 1, 2001
On Sat, 1 Dec 2001, Ed Weiland wrote:

> Not only that, baseball teams with a good winning pct.
> in one-run games generally decline the following
> season (as myself and several other White Sox fans
> found out this past summer). I suspect the same is
> true in the NBA, though I have never looked at the
> subject, nor am I aware of anyone who has.

I agree 100% with the statements that it is a mistake to look at teams'
records in close games, and to try to call the ones with good records
"clutch".

A minor quibble with the argument above however: while it is indeed true
that baseball teams with a good winning pct. in one-run games can be
expected to decline the following season, the same is true of ANY team in
ANY sport in ANY sort of games. Bill James many years ago thought he'd
discovered some profound truth in this and dreamt up some corny name for
it -- "The Law of Elastic Reboound" or something -- but it's been known
for about a century in statistics as "regression to the mean".

A team which wins 72 games in an NBA season is EXTREMELY likely to have
fewer wins the following season. A team which wins 90% of its 1-point
games in a season is extremely likely to win a lower percentage the
following season. A team which wins 70% of its 1-point games is very
likely to win a lower pct. the following season. Etc.

So while I agree 100% with both of the statements (that teams' 1-run
records have little meaning, except of course to contribute to their
win-loss record; and that teams with good 1-run records are likely to see
a decline in those records the following season), it is not the case that
the latter statement is evidence in favor of the former statement.

> I would have no idea how to analyze which players are
> clutch and which ones aren't. Basketball isn't like
> baseball where you can just look at what each player
> does in each AB and go from there. In basketball
> there's defense, rebounding and passing going on in
> addition to shooting. Those things would have to be
> looked at also, once clutch situations were defined.

True enough if we're looking for "total clutchness" but most of the NBA
players who are known as clutch are known for being clutch as shooters
during crunch time. Maybe once in a very long while they'll get a
reputation for good D in crunch time (Havlicek steals the ball, Bird
steals the ball), and I can't think of a single player who had a
reputation as a clutch rebounder. Maybe, say, Wilt, Russell, Silas, et
al -- but they were simply known as great rebounders period, it's not as
if people thought they only grabbed rebounds during crunch time and
lollygagged the rest of the game.

So to look for clutch players, I think it's an easy step to limit the
search to being a search for clutch *shooters*, and that is a more
limited, specific, easy-to-define concept.

> I've always felt "clutch" was one of those terms
> people used to describe players they wanted to like.
> Jerry West was called Mr. Clutch, despite being on the
> losing team in eight NBA finals and winning only once.
> This isn't to say West wasn't a clutch player. I just
> wonder why West got tagged with Mr. Clutch, when it
> was Bill Russell who was the biggest winner of that
> time. Probably a racial thing.

I agree with this also, although I would add the following hypothesis:
some players are given (or demand) the ball a lot in clutch situations.
And they thus shoot a lot of those crucial shots. I have no idea if some
players have a systematically higher probability of making those shots,
but if they take enough of them, some of them will go in. And people will
remember those, and tend to forget the shots that they missed. And that
will lead to the player getting a clutch reputation.

E.g. maybe Jerry West shot in his career 100 clutch shots, and made 47 of
them. That'd be identical to his career shooting percentage (both regular
season and playoff). So unless there's a tendency for clutch shots to
have a lower percentage overall (which actually might be the case), Jerry
West shot no better in clutch situations than in non-clutch. But
sportswriters, fans, and coaches would remember those 47 clutch shots
thus Jerry West would get the reputation as Mr. Clutch.

I would add that the notion that Mike Goodman and others have advocated,
of looking at playoff games as clutch situations, is I think a good one,
and the fact that West's FG% was as high in the playoffs as it was in the
regular season is in itself a fairly remarkable, one might even say
clutch, performance. Especially given that his scoring per game INCRASED.

--MKT
• ... Increased shooting could also be a case of a player trying to shoulder too much of the load. It s interesting that in West s case the season his team
Message 10 of 19 , Dec 2, 2001
>
> I would add that the notion that Mike Goodman and
> of looking at playoff games as clutch situations, is
> I think a good one,
> and the fact that West's FG% was as high in the
> playoffs as it was in the
> regular season is in itself a fairly remarkable, one
> might even say
> clutch, performance. Especially given that his
> scoring per game INCRASED.

Increased shooting could also be a case of a player
trying to shoulder too much of the load. It's
interesting that in West's case the season his team
finally broke through and won the championship, 1972,
was the only year he averaged fewer points in the
playoffs than the regular season. West also shot only
.376 during the 1972 playoffs, by far the worst
showing of his career. He did post a career playoff
high in assists per game during the '72 playoffs.

Here are some other championship performances:

Wilt in '67 averaged a then career-low 21.7 ppg in the
playoffs, shot 104 points below his regular season FG
pct. (albeit a more-than-adequate .579), but posted a
career high with 9.0 assists per game.

Hakeem in '94 and '95 had FG pct. similar to his
regular season and career totals, but posted two of
his three highest playoff assist per game totals, 4.5
and 4.3 apg, both well above his career playoff
average of 3.3. Hakeem scored 33.0 ppg in the '95
playoffs, so it's not like he was sacrificing his
shots.

I'm not sure if the spike in assists is most
responsible for the championships, but I don't think
it can be ignored. Especially considering that star
players who aren't point guards, but possessed
good-to-great passing skills like Russell, Barry,
Walton, Bird and Jordan tended to win championships.
Sometimes the the most clutch thing for a player to do
is to get his teammates involved.

btw, I don't mean to knock West as non-clutch. HIs
Laker teams lost three game sevens to the Celtics by a
involved in all that.

Ed Weiland

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• When considering clutch it seems weird to think about players actually improving over how they would in normal (nonpressure) situations. Rather, it seems to
Message 11 of 19 , Dec 2, 2001
When considering clutch it seems weird to think about players
actually improving over how they would in normal (nonpressure)
situations. Rather, it seems to me that we might better define
clutch by looking at who did not become worse in clutch situations.
How you define clutch situations, incidentally, is a question I can't

--- In APBR_analysis@y..., Ed Weiland <weiland1029@y...> wrote:
>
> >
> > I would add that the notion that Mike Goodman and
> > of looking at playoff games as clutch situations, is
> > I think a good one,
> > and the fact that West's FG% was as high in the
> > playoffs as it was in the
> > regular season is in itself a fairly remarkable, one
> > might even say
> > clutch, performance. Especially given that his
> > scoring per game INCRASED.
>
>
> Increased shooting could also be a case of a player
> trying to shoulder too much of the load. It's
> interesting that in West's case the season his team
> finally broke through and won the championship, 1972,
> was the only year he averaged fewer points in the
> playoffs than the regular season. West also shot only
> .376 during the 1972 playoffs, by far the worst
> showing of his career. He did post a career playoff
> high in assists per game during the '72 playoffs.
>
> Here are some other championship performances:
>
> Wilt in '67 averaged a then career-low 21.7 ppg in the
> playoffs, shot 104 points below his regular season FG
> pct. (albeit a more-than-adequate .579), but posted a
> career high with 9.0 assists per game.
>
> Hakeem in '94 and '95 had FG pct. similar to his
> regular season and career totals, but posted two of
> his three highest playoff assist per game totals, 4.5
> and 4.3 apg, both well above his career playoff
> average of 3.3. Hakeem scored 33.0 ppg in the '95
> playoffs, so it's not like he was sacrificing his
> shots.
>
> I'm not sure if the spike in assists is most
> responsible for the championships, but I don't think
> it can be ignored. Especially considering that star
> players who aren't point guards, but possessed
> good-to-great passing skills like Russell, Barry,
> Walton, Bird and Jordan tended to win championships.
> Sometimes the the most clutch thing for a player to do
> is to get his teammates involved.
>
> btw, I don't mean to knock West as non-clutch. HIs
> Laker teams lost three game sevens to the Celtics by a
> total of seven points. There had to be some bad luck
> involved in all that.
>
> Ed Weiland
>
> __________________________________________________
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> http://shopping.yahoo.com
• ... Here s the correlation matrix. (I hope it formats ok.) Days Dist Home MatchupP Dist 0.076 0.000 Home 0.173 -0.249 0.000 0.000
Message 12 of 19 , May 30, 2004
Dean Oliver wrote:
> Ed --
>
> Nice. Is there correlation between variables? One that is key to
> understand is whether distance from previous game and days off are
> correlated. A home stand could be hiding some aspect of time off
> between games.

Here's the correlation matrix. (I hope it formats ok.)

Days Dist Home MatchupP
Dist 0.076
0.000

Home 0.173 -0.249
0.000 0.000

MatchupP -0.014 0.021 0.001
0.509 0.315 0.975

PtsDiff 0.060 -0.041 0.226 0.465
0.003 0.048 0.000 0.000

Cell Contents: Pearson correlation
P-Value

The correlations are generally pretty low.

> (Something also irks me about the p_win variable being
> endogenous.)
>

I'm not quite sure what endogenous means. If it means being related to the
other variables, I'm not quite sure if that's true: the matchup probability
calculation uses only team winning percentage and opponent winning
percentage, neither of which have any relationship to the other variables.
Maybe I misunderstood.

> I know I did a study of time off between games and saw that there is
> an optimal period of time off (more than 2 wasn't good, but neither
> was 0). That would imply a squared term in days off. But I didn't do
> it as rigorously as you did.
>

I did something like that, too. I can't remember which season I used, but I
found that most wins came on 2 day rests (I think). However, I didn't
include any other variables, so I could have just been looking at a
scheduling quirk for that season. I'll probably rerun this study on another
season to see if the results hold. If anyone else wants to give it a shot,
here's a table showing travel distances between NBA cities:

http://members.rogers.com/brothered/junk/TravelDistances.htm

ed
• ... Yeah, pretty low. Probably not much to worry about. ... to the ... probability ... variables. ... Basically, I assume you use 2004 win-loss records to
Message 13 of 19 , May 30, 2004
--- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
<edkupfer@r...> wrote:
> Dean Oliver wrote:
> > Ed --
> >
> > Nice. Is there correlation between variables? One that is key to
> > understand is whether distance from previous game and days off are
> > correlated. A home stand could be hiding some aspect of time off
> > between games.
>
> Here's the correlation matrix. (I hope it formats ok.)
>
> Days Dist Home MatchupP
> Dist 0.076
> 0.000
>
> Home 0.173 -0.249
> 0.000 0.000
>
> MatchupP -0.014 0.021 0.001
> 0.509 0.315 0.975
>
> PtsDiff 0.060 -0.041 0.226 0.465
> 0.003 0.048 0.000 0.000
>
> Cell Contents: Pearson correlation
> P-Value
>
> The correlations are generally pretty low.
>

Yeah, pretty low. Probably not much to worry about.

> > (Something also irks me about the p_win variable being
> > endogenous.)
> >
>
> I'm not quite sure what endogenous means. If it means being related
to the
> other variables, I'm not quite sure if that's true: the matchup
probability
> calculation uses only team winning percentage and opponent winning
> percentage, neither of which have any relationship to the other
variables.
> Maybe I misunderstood.

Basically, I assume you use 2004 win-loss records to evaluate p_win.
Well, those win-loss records are built from the things you are looking
at -- whether a team is at home or on the road, how many days off,
their whole schedule. Maybe the win-loss records of teams prior to
the matchup of the game you're looking at is exogenous (known a
priori). i.e., San Antonio faces the Lakers when one team is 12-5 and
the other is 10-7 -- use those records rather than their end of season
records. Maybe that's what you're doing, I dunno. I have doubt that
it would make a significant difference.

>
>
> > I know I did a study of time off between games and saw that there is
> > an optimal period of time off (more than 2 wasn't good, but neither
> > was 0). That would imply a squared term in days off. But I didn't do
> > it as rigorously as you did.
> >
>
> I did something like that, too. I can't remember which season I
used, but I
> found that most wins came on 2 day rests (I think). However, I didn't
> include any other variables, so I could have just been looking at a
> scheduling quirk for that season. I'll probably rerun this study on
another
> season to see if the results hold.

Just include the variable Days^2 in your regression and rerun that.
See what comes out significant.

DeanO

Dean Oliver
"Oliver goes beyond stats to dissect what it takes to win. His breezy
style makes for enjoyable reading, but there are plenty of points of
wisdom as well. This book can be appreciated by fans, players,
coaches and executives, but more importantly it can be used as a text
book for all these groups. You are sure to learn something you didn't
Baseball and Hidden Game of Football
• Dean Oliver wrote: ... Ah. I will try to use contemporary win/loss records in my next analysis. ... You ll have to help me out here, as I don t
Message 14 of 19 , May 30, 2004
Dean Oliver wrote:
<snip>

>>>
>>
>> I'm not quite sure what endogenous means. If it means being related
> to the
>> other variables, I'm not quite sure if that's true: the matchup
>> probability calculation uses only team winning percentage and
>> opponent winning percentage, neither of which have any relationship
>> to the other variables. Maybe I misunderstood.
>
> Basically, I assume you use 2004 win-loss records to evaluate p_win.
> Well, those win-loss records are built from the things you are looking
> at -- whether a team is at home or on the road, how many days off,
> their whole schedule. Maybe the win-loss records of teams prior to
> the matchup of the game you're looking at is exogenous (known a
> priori). i.e., San Antonio faces the Lakers when one team is 12-5 and
> the other is 10-7 -- use those records rather than their end of season
> records. Maybe that's what you're doing, I dunno. I have doubt that
> it would make a significant difference.

Ah. I will try to use contemporary win/loss records in my next analysis.

<snip>

> Just include the variable Days^2 in your regression and rerun that.
> See what comes out significant.
>

You'll have to help me out here, as I don't know anything about transforming
data. Do you mean include Days^2 in addition to Days or instead of Days? I
did both, and here's how they turned out:

PtsDiff = - 21.2 + 2.59 Days +0.000021 Dist + 5.59 Home + 30.0 MatchupP -
0.394 Days_2

Predictor Coef SE Coef T P
Constant -21.169 1.195 -17.71 0.000
Days 2.5924 0.7956 3.26 0.001
Dist 0.0000214 0.0003555 0.06 0.952
Home 5.5937 0.4858 11.51 0.000
MatchupP 29.991 1.134 26.45 0.000
Days_2 -0.3938 0.1350 -2.92 0.004

PtsDiff = - 18.1 +0.000099 Dist + 5.87 Home + 29.9 MatchupP + 0.0236 Days_2

Predictor Coef SE Coef T P
Constant -18.0811 0.7304 -24.76 0.000
Dist 0.0000990 0.0003555 0.28 0.781
Home 5.8677 0.4794 12.24 0.000
MatchupP 29.890 1.136 26.32 0.000
Days_2 0.02362 0.04275 0.55 0.581

ed
• ... I should note that, not being an economist, I like throwing this word around without as great an appreciation or understanding for it as I should. ...
Message 15 of 19 , May 30, 2004
--- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
<edkupfer@r...> wrote:
> >> I'm not quite sure what endogenous means. If it means being

I should note that, not being an economist, I like throwing this word
around without as great an appreciation or understanding for it as I
should.

> You'll have to help me out here, as I don't know anything about
transforming
> data. Do you mean include Days^2 in addition to Days or instead of
Days? I
> did both, and here's how they turned out:

Include both, which you did in the first set below. Looks like it got
you significant on both days and days^2. And the signs are as
expected. It suggests optimal rest at about 3 days, longer than the 2
days we saw before. (Potentially important for the talk about rust vs
rest, esp if the Lakers wrap up on M.) Let me also ask -- is Days = 0
if a team plays back to back nights or is that Days = 1?

I'm sure there are other ways to manipulate things, but this looks
like a pretty good thing. I'm saving it.

Home is a binary 1/0 indicator for home/road, resp?

>
> PtsDiff = - 21.2 + 2.59 Days +0.000021 Dist + 5.59 Home + 30.0
MatchupP -
> 0.394 Days_2
>
> Predictor Coef SE Coef T P
> Constant -21.169 1.195 -17.71 0.000
> Days 2.5924 0.7956 3.26 0.001
> Dist 0.0000214 0.0003555 0.06 0.952
> Home 5.5937 0.4858 11.51 0.000
> MatchupP 29.991 1.134 26.45 0.000
> Days_2 -0.3938 0.1350 -2.92 0.004
>
>

DeanO

Dean Oliver
"Dean Oliver looks at basketball with a fresh perspective. If you
want a new way to analyze the game, this book is for you. You'll
never watch a game the same way again. We use his stuff and it helps
us." Yvan Kelly, Scout, Seattle Sonics
• Okay, I ran the test again, this time using 03-04 results. Before I show you what I got, let me address a couple of things. ... Hell, that s nothing. Once
Message 16 of 19 , May 31, 2004
Okay, I ran the test again, this time using 03-04 results. Before I show you
what I got, let me address a couple of things.

Dean Oliver wrote:
> --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
> <edkupfer@r...> wrote:
>>>> I'm not quite sure what endogenous means. If it means being
>
> I should note that, not being an economist, I like throwing this word
> around without as great an appreciation or understanding for it as I
> should.

Hell, that's nothing. Once during the course of an argument with an
ex-girlfriend I used the word "heretofore." I still don't know what it
means.

>
>> You'll have to help me out here, as I don't know anything about
>> transforming data. Do you mean include Days^2 in addition to Days or
>> instead of Days? I did both, and here's how they turned out:
>
> Include both, which you did in the first set below. Looks like it got
> you significant on both days and days^2. And the signs are as
> expected. It suggests optimal rest at about 3 days, longer than the 2
> days we saw before. (Potentially important for the talk about rust vs
> rest, esp if the Lakers wrap up on M.)

Questions: I don't understand a couple of things about the squared term. How
did you know that squaring the Days variable would give a better fit? And,
just exactly how does it suggest the optimal 3 day rest?

> Let me also ask -- is Days = 0
> if a team plays back to back nights or is that Days = 1?
>

The latter. I am subtracting game dates from each other.

> I'm sure there are other ways to manipulate things, but this looks
> like a pretty good thing. I'm saving it.
>
> Home is a binary 1/0 indicator for home/road, resp?

Yes.

Okay. Here are the results for 03-04. For the Matchup Probability, I used
the team records heading into the game. For example, for two teams playing
their first games of the season, I would use 0-0 records for each team in my
probability calculation. Interestingly, this doesn't seem to affect the
regression results too much. The effect of Days between games is reduced in
this sample. Weird.

PtsDiff = - 13.6 + 7.31 Home +0.000027 Distance + 18.1 WinProb + 0.722
Days - 0.122 Days^2

Predictor Coef SE Coef T P
Constant -13.582 1.173 -11.58 0.000
Home 7.3056 0.5010 14.58 0.000
Distance 0.0000269 0.0003734 0.07 0.943
WinProb 18.054 1.163 15.53 0.000
Days 0.7221 0.7202 1.00 0.316
Days^2 -0.1216 0.1138 -1.07 0.286

S = 11.48 R-Sq = 16.7% R-Sq(adj) = 16.6%

Analysis of Variance

Source DF SS MS F P
Regression 5 62072 12414 94.19 0.000
Residual Error 2343 308806 132
Total 2348 370877

ed
• ... show you ... I ve had those moments, often inspired by arguments with soon-to-be ex-girlfriends. What the hell is vis-a-vis ? ... term. How ... fit? And,
Message 17 of 19 , May 31, 2004
--- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
<edkupfer@r...> wrote:
> Okay, I ran the test again, this time using 03-04 results. Before I
show you
> what I got, let me address a couple of things.
>
> Dean Oliver wrote:
> > --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
> > <edkupfer@r...> wrote:
> >>>> I'm not quite sure what endogenous means. If it means being
> >
> > I should note that, not being an economist, I like throwing this word
> > around without as great an appreciation or understanding for it as I
> > should.
>
> Hell, that's nothing. Once during the course of an argument with an
> ex-girlfriend I used the word "heretofore." I still don't know what it
> means.

I've had those moments, often inspired by arguments with soon-to-be
ex-girlfriends. What the hell is "vis-a-vis"?

> >
> >> You'll have to help me out here, as I don't know anything about
> >> transforming data. Do you mean include Days^2 in addition to Days or
> >> instead of Days? I did both, and here's how they turned out:
> >
> > Include both, which you did in the first set below. Looks like it got
> > you significant on both days and days^2. And the signs are as
> > expected. It suggests optimal rest at about 3 days, longer than the 2
> > days we saw before. (Potentially important for the talk about rust vs
> > rest, esp if the Lakers wrap up on M.)
>
> Questions: I don't understand a couple of things about the squared
term. How
> did you know that squaring the Days variable would give a better
fit? And,
> just exactly how does it suggest the optimal 3 day rest?

I didn't _know_ it would give a better fit. I hoped it would because
of what we were observing -- that there was an optimal number of days
off. The only way to get an optimum out of a regression is to throw
in higher order terms. Usually a squared term is plenty. It doesn't
answer the bigger question of whether teams get rusty, though. It
suggests an answer (another lesson in how to lie with statistics), one
that I wouldn't trust from this study.

Look at the results of your regression. Take just the Days and Days^2
coefficients and calculate the marginal net points those terms
contribute for Days = 1, 2, 3, 4, etc. You'll see a max at 3.

>
> > Let me also ask -- is Days = 0
> > if a team plays back to back nights or is that Days = 1?
> >
>
> The latter. I am subtracting game dates from each other.
>

So 2 days of rest is optimal.

> > I'm sure there are other ways to manipulate things, but this looks
> > like a pretty good thing. I'm saving it.
> >
> > Home is a binary 1/0 indicator for home/road, resp?
>
> Yes.
>
> Okay. Here are the results for 03-04. For the Matchup Probability, I
used
> the team records heading into the game. For example, for two teams
playing
> their first games of the season, I would use 0-0 records for each
team in my
> probability calculation.

I was curious to see how you handled the early games of the season,
especially the times where one team was undefeated. It looks like you
used Pythagorean projections, rather than real records anyway. That
helps. But 0-0 usually requires some other assumption, like a
Bayesian prior that carries through the first few games.

>Interestingly, this doesn't seem to affect the
> regression results too much. The effect of Days between games is
reduced in
> this sample. Weird.

Not sure what to make of that weakening of the Days. What was the R2
of the previous version? We may have to improve the prior matchup P
to get back a reasonable estimate of the value of Days. If you just
look at games beyond the first 20 in the season, does r2 get better
and does Days become more significant?

>
>
> PtsDiff = - 13.6 + 7.31 Home +0.000027 Distance + 18.1 WinProb + 0.722
> Days - 0.122 Days^2
>
> Predictor Coef SE Coef T P
> Constant -13.582 1.173 -11.58 0.000
> Home 7.3056 0.5010 14.58 0.000
> Distance 0.0000269 0.0003734 0.07 0.943
> WinProb 18.054 1.163 15.53 0.000
> Days 0.7221 0.7202 1.00 0.316
> Days^2 -0.1216 0.1138 -1.07 0.286
>
> S = 11.48 R-Sq = 16.7% R-Sq(adj) = 16.6%
>
> Analysis of Variance
>
> Source DF SS MS F P
> Regression 5 62072 12414 94.19 0.000
> Residual Error 2343 308806 132
> Total 2348 370877
>

DeanO

Dean Oliver
"Excellent writing. There are a lot of math guys who just rush from
the numbers to the conclusion. . .they'll tell you that Shaq is a real
good player but his team would win a couple more games a year if he
could hit a free throw. Dean is more than that; he's really
struggling to understand the actual problem, rather than the
statistical after-image of it. I learn a lot by reading him." Bill
James, author Baseball Abstract
• Replies to DanR and DeanO ... I m sorry I didn t make it clear. For the second analysis (on the 03-04 regular season results) I didn;t use Pythagorean records.
Message 18 of 19 , Jun 2 2:03 PM
Replies to DanR and DeanO

Dean Oliver wrote:

> I was curious to see how you handled the early games of the season,
> especially the times where one team was undefeated. It looks like you
> used Pythagorean projections, rather than real records anyway. That
> helps. But 0-0 usually requires some other assumption, like a
> Bayesian prior that carries through the first few games.

I'm sorry I didn't make it clear. For the second analysis (on the 03-04
regular season results) I didn;t use Pythagorean records. I instead used
each team's record to date. Two teams facing each other on the first game of
the season each had a 0.5 chance of winning that game, since they had
identical 0-0 records.

The results don't deviate much from my first analysis, which used season's
end Pythagorean Win%. I supposed this is because after the first part of the
season, each team's Pyth is relatively stable. I must admit to being a
little surprised by this, though.

> Not sure what to make of that weakening of the Days. What was the R2
> of the previous version?

r = 0.06 for 00-01, r = 0.03 for this season.

> We may have to improve the prior matchup P
> to get back a reasonable estimate of the value of Days. If you just
> look at games beyond the first 20 in the season, does r2 get better
> and does Days become more significant?
>

Games 2-20: r = 0.048 (p = 0.261)
Games 21-82: r = 0.024 (p = 0.314

dan_t_rosenbaum wrote:

> Interesting results. Here are a couple of suggestions.
>
> I would leave out the MatchupP variable, since it is a lot like the
> dependent variable. Including it probably increases R-squared a
> lot, but probably doesn't do much else. (All in all, it probably is
> pretty harmless, since it unlikely to be correlated with your
> independent variables.)
>
> Another option with your day variable is to enter it as a series of
> dummy variables.
>
> DAY0 - equals 1 if 0 days of rest, 0 otherwise
> DAY1 - equals 1 if 1 day of rest , 0 otherwise
> DAY2 - equals 1 if 2 days of rest, 0 otherwise
> DAY3 - equals 1 if 3 days of rest, 0 otherwise
> DAY4+ - equals 1 if 4 days or more of rest, 0 otherwise
>
> Then run the regression leaving one of those variables out.
>
> If, for example, you left DAY0 out of the regression, the DAY1
> coefficient would give you the effect of playing on one day's rest
> versus playing in a back-to-back.
>
> The DAY2 coefficent would give you the effect of playing on two
> days' rest versus playing in a back-to-back.
>
> The DAY3 coefficent would give you the effect of playing on three
> days' rest versus playing in a back-to-back.
>
> The DAY4+ coefficent would give you the effect of playing on four or
> more days' rest versus playing in a back-to-back.
>

Okay, I tried this. The regression outputs follow. I'm afraid that I don't
know how to interpret the results -- very few of the coefficients are
significant. (Note that I use Day1 to mean 1 day between games, ie back to
back -- the 1 does not mean "rest days.")

Ommitting Days1

Predictor Coef SE Coef T P
Constant -3.8515 0.5971 -6.45 0.000
Home 7.1257 0.5267 13.53 0.000
Distance -0.0000105 0.0003920 -0.03 0.979
Days2 0.5837 0.6157 0.95 0.343
Days3 0.3022 0.7996 0.38 0.706
Days4 -2.363 1.394 -1.70 0.090
Days5+ 0.935 1.801 0.52 0.604

Omitting Days2

Predictor Coef SE Coef T P
Constant -3.2678 0.5624 -5.81 0.000
Home 7.1257 0.5267 13.53 0.000
Distance -0.0000105 0.0003920 -0.03 0.979
Days1 -0.5837 0.6157 -0.95 0.343
Days3 -0.2815 0.6981 -0.40 0.687
Days4 -2.947 1.338 -2.20 0.028
Days5+ 0.351 1.758 0.20 0.842

Omitting Days3

Predictor Coef SE Coef T P
Constant -3.5494 0.7833 -4.53 0.000
Home 7.1257 0.5267 13.53 0.000
Distance -0.0000105 0.0003920 -0.03 0.979
Days1 -0.3022 0.7996 -0.38 0.706
Days2 0.2815 0.6981 0.40 0.687
Days4 -2.665 1.426 -1.87 0.062
Days5+ 0.633 1.824 0.35 0.729

Omitting Days4

Predictor Coef SE Coef T P
Constant -6.215 1.385 -4.49 0.000
Home 7.1257 0.5267 13.53 0.000
Distance -0.0000105 0.0003920 -0.03 0.979
Days1 2.363 1.394 1.70 0.090
Days2 2.947 1.338 2.20 0.028
Days3 2.665 1.426 1.87 0.062
Days5+ 3.298 2.152 1.53 0.126

Omitting Days5+

Predictor Coef SE Coef T P
Constant -2.917 1.799 -1.62 0.105
Home 7.1257 0.5267 13.53 0.000
Distance -0.0000105 0.0003920 -0.03 0.979
Days1 -0.935 1.801 -0.52 0.604
Days2 -0.351 1.758 -0.20 0.842
Days3 -0.633 1.824 -0.35 0.729
Days4 -3.298 2.152 -1.53 0.126

ed
• ... http://www.shrpsports.com/nba/stand/2002.htm -- ed
Message 19 of 19 , Nov 2, 2004
ivan ivan wrote:
> this is a simple question
> but i can't find it anywhere....
>
>
> I'm doing analysis on how a history of winning or losing affects your
> chances of winning at the end of close games... so does anyone know
> where i can standings for the 2001-2002 NBA season?
> i want the home and away records?
>

http://www.shrpsports.com/nba/stand/2002.htm

--
ed
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